The inhibition of ecdysone signal pathway was the key of pyriproxyfen poisoning for silkworm, Bombyx mori.

Pyriproxyfen is a juvenile hormone-like pesticide. Once intake occurs, it leads to a series of poisoning characters consequences in silkworm, Bombyx mori (ID: 7091, Lepidoptera), such as non- cocooning, non-pupation, production of low-active eggs, and extended stages. However, the poisoning mechanism is still unclear. Here, silkworms were fed mulberry leaves soaked with different pyriproxyfen concentrations, and the heads were dissected for transcriptome analysis, while the hemolymph was used for determinations of ecdysone and juvenile hormone titers. As a result, after conjoint analysis of 3 feeding groups and a control group, 555 differentially expressed genes (DEGs) were obtained, which were mainly involved in hormone metabolism, glycometabolism and protein metabolism. Meanwhile, 119 genes were significantly correlated with the pyriproxyfen concentrations, and they were mainly involved in drug metabolism and glycometabolism. The ecdysone titers in several feeding groups were significantly lower than those of the control group, while juvenile hormone was not detected in all groups, including the control and feeding groups. Correspondingly, due to activation of the juvenile hormone signaling pathway by pyriproxyfen, key genes in the ecdysone synthesis pathway were downregulated, and a large number of downstream genes were up- or downregulated. In addition, nearly all genes in the detoxification pathway were upregulated. These results suggested that, affected by the juvenile hormone signaling pathway, ecdysone titers decreased and further affected a series of downstream processes, and this was the key reason for pyriproxyfen poisoning in silkworm, B. mori, which could lay a foundation for the study of pyriproxyfen resistance in silkworm.

Identification of a microsporidian isolate from Cnaphalocrocis Medinalis and its pathogenicity to Bombyx mori.

A microsporidian, CmM2, was isolated from Cnaphalocrocis medinalis. The biological characters, molecular analysis and pathogenicity of CmM2 were studied. The spore of CmM2 is long oval in shape and 3.45 ± 0.25 × 1.68 ± 0.18 µm in size, the life cycle includes meronts, sporonts, sporoblasts, and spores, with typical diplokaryon in each stage, propagated in binary fission. There is positive coagulation reaction between CmM2 and the polyclonal antibody of Nosema bombycis (N.b.). CmM2 spores is binuclear, and has 10-12 polar filament coils. The small subunit ribosomal RNA (SSU rRNA) gene sequence of CmM2 was obtained by PCR amplification and sequencing, the phylogenetic tree based on SSU rRNA sequences had been constructed, and the similarity and genetic distance of SSU rRNA sequences were analyzed, showed that CmM2 was grouped in the Nosema clade. The 50% infectious concentration of CmM2 to Bombyx mori is 4.72 × 10(4) spores ml(-1) , and the germinative infection rate is 12.33%. The results showed that CmM2 is classified into genus Nosema, as Nosema sp. CmM2, and has a heavy infectivity to B. mori. The result indicated as well that it is valuable taxonomic determination for microsporidian isolates based on both biological characters and molecular evidence.

Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Exploring new insights on genetic factors may help reveal SLE etiology and improve the survival of SLE patients. The current study is designed to identify key genes involved in SLE and develop potential diagnostic biomarkers for SLE in clinical practice. Expression data of all genes of SLE and control samples in GSE65391 and GSE72509 datasets were downloaded from the Gene Expression Omnibus (GEO) database. A total of 11 accurate differentially expressed genes (DEGs) were identified by the "limma" and "RobustRankAggreg" R package. All these genes were functionally associated with several immune-related biological processes and a single KEGG (Kyoto Encyclopedia of Genes and Genome) pathway of necroptosis. The PPI analysis showed that IFI44, IFI44L, EIF2AK2, IFIT3, IFITM3, ZBP1, TRIM22, PRIC285, XAF1, and PARP9 could interact with each other. In addition, the expression patterns of these DEGs were found to be consistent in GSE39088. Moreover, Receiver operating characteristic (ROC) curves analysis indicated that all these DEGs could serve as potential diagnostic biomarkers according to the area under the ROC curve (AUC) values. Furthermore, we constructed the transcription factor (TF)-diagnostic biomarker-microRNA (miRNA) network composed of 278 nodes and 405 edges, and a drug-diagnostic biomarker network consisting of 218 nodes and 459 edges. To investigate the relationship between diagnostic biomarkers and the immune system, we evaluated the immune infiltration landscape of SLE and control samples from GSE6539. Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our findings may benefit the diagnosis of patients with SLE and guide in developing novel targeted therapy in treating SLE patients.

Impacts of rock properties on Danxia landform formation based on lithological experiments at Kongtongshan National Geopark, northwest China.

As an erosional landform, the formation processes of Danxia landform are controlled by internal and external forces as well as lithologic properties. Using field data, we studied the role of lithologic properties on the formation of Danxia landform in Kongtongshan National Geopark, northwest China, through a series of experiments, including uniaxial compressive strength, identification analysis under polarizing microscope, X-ray diffraction analysis, inductively coupled plasma-mass spectrometry analysis, and scanning electron microscopy. The results show that the diagenesis degree, mineral composition, cement composition, degree of cementation, geochemical composition and element contents, and micro-structure influenced the structure and anti-weathering and anti-erosion abilities of the Danxia rock mass. Differential weathering of rock in different environments was an important force shaping the different types of Danxia landform. Weathering failure of the Danxia rock mass was the result of multiple combined factors; as well as lithology, other factors, such as those induced during tectonic uplift (i.e., faulting, jointing, and fracturing) and climate, cannot be neglected. Therefore, lithology played an important role in the structural development of Danxia landform, and different lithologies influenced its weathering rate and formation processes. Our findings can provide a reference for revealing the microscopic development of Danxia landform in arid and semi-arid areas.

A geometric framework for ensemble average propagator reconstruction from diffusion MRI.

Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive technique to probe the complex micro-architecture of the tissue being imaged. The diffusional properties of the tissue at the imaged resolution are well captured by the ensemble average propagator (EAP), which is a probability density function characterizing the probability of water molecule diffusion. Many properties in the form of imaging 'stains' can then be computed from the EAP that can serve as bio-markers for a variety of diseases. This motivates the development of methods for the accurate estimation of the EAPs from dMRI, which is an actively researched area in dMRI analysis. To this end, in the recent past, dictionary learning (DL) techniques have been applied by many researchers for accurate reconstruction of the EAP fields from dMRI scans of the central nervous system (CNS). However, most of the DL-based methods did not exploit the geometry of the space of the EAPs, which are probability density functions. By exploiting the geometry of the space of probability density functions, it is possible to reconstruct EAPs that satisfy the mathematical properties of a density function and hence yield better accuracy in the EAP field reconstruction. Using a square root density parameterization, the EAPs can be mapped to a unit Hilbert sphere, which is a smooth manifold with well known geometry that we will exploit in our formulation of the DL problem. Thus, in this paper, we present a general formulation of the DL problem for data residing on smooth manifolds and in particular the manifold of EAPs, along with a numerical solution using an alternating minimization method. We then showcase the properties and the performance of our algorithm on the reconstruction of the EAP field in a patch-wise manner from the dMRI data. Through several synthetic, phantom and real data examples, we demonstrate that our non-linear DL-based approach produces accurate and spatially smooth estimates of the EAP field from dMRI in comparison to the state-of-the-art EAP reconstruction method called the MAPL method, as well as the linear DL-based EAP reconstruction approaches. To further demonstrate the accuracy and utility of our approach, we compute an entropic anisotropy measure (HA), that is a function of the well known Rényi entropy, from the EAP fields of control and injured rat spinal cords respectively. We demonstrate its utility as an imaging 'stain' via a quantitative comparison of HA maps computed from EAP fields estimated using our method and competing methods. The quantitative comparison is achieved using a two sample t-test and the results of significance are displayed for a visualization of regions of the spinal cord affected most by the injury.